Vehicle spatiotemporal distribution identification in low-light environment based on image enhancement and object detection

Abstract

The spatiotemporal distribution of vehicles on roads and bridges is important for the operation and maintenance of transportation systems. The accuracy of vehicle identification is affected by the lighting conditions, especially low-light environments. This study proposes a vehicle spatiotemporal distribution identification method using image enhancement and object detection. First, the FP-ZeroDCE algorithm is used to enhance low-light images, which improves the brightness and contrast of images. Next, the enhanced images are input into the AFF-YOLO model to identify the spatiotemporal distribution of vehicles. Finally, the proposed method is validated using public datasets and tested in the field. The results indicate that the proposed method can enhance the quality of low-light images, with an increase in the Peak Signal-to-Noise Ratio by 8.257 dB, and improve the accuracy of vehicle detection, with an accuracy of 92.7 %. The proposed method is an effective means for identifying vehicle spatiotemporal distribution under low-light conditions.